tableDP: Differentially Private Contingency Table

In DPpack: Differentially Private Statistical Analysis and Machine Learning

Differentially Private Contingency Table

Description

This function computes a differentially private contingency table from given vectors of data at user-specified privacy levels of epsilon and delta.

Usage

tableDP(
  ...,
  eps = 1,
  which.sensitivity = "bounded",
  mechanism = "Laplace",
  delta = 0,
  type.DP = "aDP",
  allow.negative = FALSE
)

Arguments

...	Vectors of data from which to create the contingency table.
eps	Positive real number defining the epsilon privacy budget.
which.sensitivity	String indicating which type of sensitivity to use. Can be one of {'bounded', 'unbounded', 'both'}. If 'bounded' (default), returns result based on bounded definition for differential privacy. If 'unbounded', returns result based on unbounded definition. If 'both', returns result based on both methods \insertCiteKifer2011DPpack. Note that if 'both' is chosen, each result individually satisfies (eps, delta)-differential privacy, but may not do so collectively and in composition. Care must be taken not to violate differential privacy in this case.
mechanism	String indicating which mechanism to use for differential privacy. Currently the following mechanisms are supported: {'Laplace', 'Gaussian', 'analytic'}. Default is Laplace. See LaplaceMechanism and GaussianMechanism for descriptions of the supported mechanisms.
delta	Nonnegative real number defining the delta privacy parameter. If 0 (default), reduces to eps-DP.
type.DP	String indicating the type of differential privacy desired for the Gaussian mechanism (if selected). Can be either 'pDP' for probabilistic DP \insertCiteMachanavajjhala2008DPpack or 'aDP' for approximate DP \insertCiteDwork2006bDPpack. Note that if 'aDP' is chosen, epsilon must be strictly less than 1.
allow.negative	Logical value. If FALSE (default), any negative values in the sanitized table due to the added noise will be set to 0. If TRUE, the negative values (if any) will be returned.

Value

Sanitized contingency table based on the bounded and/or unbounded definitions of differential privacy.

References

\insertRef

Dwork2006aDPpack

\insertRef

Kifer2011DPpack

\insertRef

Machanavajjhala2008DPpack

\insertRef

Dwork2006bDPpack

Examples

x <- MASS::Cars93$Type
y <- MASS::Cars93$Origin
z <- MASS::Cars93$AirBags
tableDP(x,y,eps=1,which.sensitivity='bounded',mechanism='Laplace',
  type.DP='pDP')
tableDP(x,y,z,eps=.5,which.sensitivity='unbounded',mechanism='Gaussian',
  delta=0.01)

DPpack documentation built on Oct. 20, 2024, 9:07 a.m.